{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-10T07:41:16.180422Z",
     "start_time": "2019-02-10T07:29:31.517239Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data size 1786004\n",
      "Most common words (+UNK) [['UNK', 1195], ('。', 149620), ('，', 108451), ('、', 19612), ('人', 13607)]\n",
      "Sample data [0, 1502, 1827, 39, 612, 46, 8, 110, 116, 7] ['UNK', '潘', '阆', '酒', '泉', '子', '（', '十', '之', '一']\n",
      "1502 潘 -> 0 UNK\n",
      "1502 潘 -> 1827 阆\n",
      "1827 阆 -> 1502 潘\n",
      "1827 阆 -> 39 酒\n",
      "39 酒 -> 612 泉\n",
      "39 酒 -> 1827 阆\n",
      "612 泉 -> 39 酒\n",
      "612 泉 -> 46 子\n",
      "WARNING:tensorflow:From <ipython-input-1-9b55d91ec652>:197: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "Initialized\n",
      "Average loss at step  0 :  194.487060546875\n",
      "Nearest to 好: 迢, 姻, 祷, 与, 辱, 鳷, 张, 巉,\n",
      "Nearest to 何: 款, 遮, 咭, 翛, 讼, m, 担, 逦,\n",
      "Nearest to 得: 曜, 跗, 照, 瑶, 口, 饴, 属, 旗,\n",
      "Nearest to 情: 麦, 乳, 镗, 侬, 隔, 巷, 偶, 海,\n",
      "Nearest to 江: 贼, 鹁, 仁, 饮, 隐, 爷, 射, 张,\n",
      "Nearest to 来: 蕤, 颦, 膂, 萧, 醺, 鷃, 涧, 剖,\n",
      "Nearest to 声: 噫, 蓟, 远, 蒂, 忻, 籋, 蕲, 史,\n",
      "Nearest to 香: 隔, 暮, 密, 泫, 磷, 殇, 鲙, 洒,\n",
      "Nearest to 流: 国, 阖, 蹲, 亩, 劣, 渝, 叫, 钚,\n",
      "Nearest to 愁: 庚, 元, 阓, 泸, 斟, 酺, 备, 匀,\n",
      "Nearest to UNK: 柔, ■, 拘, 蕾, 迨, 赠, 渗, 镵,\n",
      "Nearest to 多: 籍, 眉, 噫, 浒, 膻, 唾, 愈, 验,\n",
      "Nearest to 清: ＊, 全, 胎, 罨, 蝀, 途, 琮, 舂,\n",
      "Nearest to 。: 碾, 载, 仔, 膝, 蜡, 鹓, 且, 透,\n",
      "Nearest to 似: 营, 熳, 瘁, 凛, 僝, 狐, 褥, 琛,\n",
      "Nearest to 如: 褰, 躔, 悄, 染, 罕, 沩, 跪, 运,\n",
      "Average loss at step  2000 :  21.88498501586914\n",
      "Average loss at step  4000 :  5.347439922213554\n",
      "Average loss at step  6000 :  4.978275085926056\n",
      "Average loss at step  8000 :  4.803861359000206\n",
      "Average loss at step  10000 :  4.6827685601711275\n",
      "Nearest to 好:  , 与, 宁, 祷, 迢, 璧, 袅, 堂,\n",
      "Nearest to 何:  , 款, 遮, 饼, 逦, 未, 抵, 恼,\n",
      "Nearest to 得: 曜, 口, 照,  , 峰, 属, 缠, 个,\n",
      "Nearest to 情: 乳, 窈, 觑, 麦, 隔, 侬, 海, 愁,\n",
      "Nearest to 江:  , 贼, 张, 射, 潘, 饮, 蛇, 豆,\n",
      "Nearest to 来: 颦, 觉,  , 醺, 淮, 剖, 今, 刺,\n",
      "Nearest to 声: 史, 皓, 忔, 瞋, 酬,  , 远, 心,\n",
      "Nearest to 香: 隔, 这, 密, 滋, 洒, 塘,  , 暮,\n",
      "Nearest to 流: 国, 阖, 劣,  , 叫, 陌, 钚, 蹲,\n",
      "Nearest to 愁:  , 匀, 斟, 元, 情, 懒, 泸, 真,\n",
      "Nearest to UNK: 柔, 膝, 拘, 则, 赠, 蕾, 尤, 聚,\n",
      "Nearest to 多: 籍, 许, 缨, 噫, 眉, 磔, 验, 兴,\n",
      "Nearest to 清: 全, 舂, 帝,  , 胎, 蛛, 近, 臾,\n",
      "Nearest to 。: ，, 、,  , 鸪, 缺, 钝, 枨, ）,\n",
      "Nearest to 似: 营, 动,  , ，, 熳, 臆, 幰, 僝,\n",
      "Nearest to 如: 染, 桥, 躔, 悄, ，, 鲁, 罕, 褰,\n",
      "Average loss at step  12000 :  4.623712114930153\n",
      "Average loss at step  14000 :  4.5613161950111385\n",
      "Average loss at step  16000 :  4.556626190423965\n",
      "Average loss at step  18000 :  4.548156671524048\n",
      "Average loss at step  20000 :  4.490752802610397\n",
      "Nearest to 好:  , 宁, 与, 祷, 璧, 吾, 便, 受,\n",
      "Nearest to 何:  , 款, 遮, 逦, 恼, 不, 饼, 拓,\n",
      "Nearest to 得: 曜, 蛙, 照,  , 口, 戌, 属, 弟,\n",
      "Nearest to 情: 乳, 愁, 觑, 窈, 筹, 某, 隔, 侬,\n",
      "Nearest to 江: 贼,  , 张, 射, 爷, 水, 鳊, 澌,\n",
      "Nearest to 来: 今, 觉,  , 也, 刺, 醺, 颦, 鷃,\n",
      "Nearest to 声: 史, 忔, 漏, 徵, 费, 泓, 器, 傲,\n",
      "Nearest to 香: 隔, 这, 滋, 密, 乌,  , 塘, 犯,\n",
      "Nearest to 流: 国, 阖, 劣,  , 渝, 陌, 叫, 蹲,\n",
      "Nearest to 愁:  , 情, 懒, 元, 匀, 斟, 长, 真,\n",
      "Nearest to UNK: 柔, 膝, 拘, ■, 蕾, 赠, 则, 聚,\n",
      "Nearest to 多: 籍, 噫, 验, 磔, 缨, 时, 许, 窟,\n",
      "Nearest to 清: 全, 舂, 帝, 胎, 孚,  , 蛛, 臾,\n",
      "Nearest to 。: ，, 、,  , 钝, 鸪, ）, 贬, 缙,\n",
      "Nearest to 似: 营, 动, 臆, 熳, 醴,  , 妾, 凛,\n",
      "Nearest to 如: 染, ，, 浇, 觉, 躔, 舫, 鲁, 轺,\n",
      "Average loss at step  22000 :  4.4395154082775115\n",
      "Average loss at step  24000 :  4.465362704277038\n",
      "Average loss at step  26000 :  4.514244161009788\n",
      "Average loss at step  28000 :  4.446053348898888\n",
      "Average loss at step  30000 :  4.2498206111192705\n",
      "Nearest to 好: 宁,  , 与, 酊, 受, 便, 吾, 鞠,\n",
      "Nearest to 何: 囗,  , 款, 不, 饼, 恼, 逦, 遮,\n",
      "Nearest to 得: 取,  , 触, 属, 曜, 杀, 照, 蛙,\n",
      "Nearest to 情: 愁, 某, 乳, 觑, 窈, 肠, 筹, 会,\n",
      "Nearest to 江: 贼,  , 水, 张, 爷, 鳊, 澌, 射,\n",
      "Nearest to 来: 今,  , 刺, 觉, 也, 去, 醺, 犁,\n",
      "Nearest to 声: 史, 忔, 徵, 漏, 费, 言, 傲, 破,\n",
      "Nearest to 香: 隔, 这, 密, 霜,  , 磷, 麟, 醑,\n",
      "Nearest to 流: 国, 阖, 劣, 渝,  , 叫, 霎, 东,\n",
      "Nearest to 愁:  , 情, 懒, 嗔, 斟, 等, 长, 肃,\n",
      "Nearest to UNK: 柔, ■, 膝, 拘, 赠, 聚, 隘, 则,\n",
      "Nearest to 多: 籍, 噫, 时, 磔, 验, 及, 缨, 许,\n",
      "Nearest to 清: 全, 舂,  , 胎, 帝, 孚, 蛛, 於,\n",
      "Nearest to 。: ，, 、,  , 鸪, 萨, 粳, 缙, ）,\n",
      "Nearest to 似: 营, 如, 臆, 动, 醴, 熳,  , 记,\n",
      "Nearest to 如: 染, 似, 罕, 滁, 轺, 觉, 鲁, 资,\n",
      "Average loss at step  32000 :  4.2903399797678\n",
      "Average loss at step  34000 :  4.304637260079383\n",
      "Average loss at step  36000 :  4.277668159127235\n",
      "Average loss at step  38000 :  4.271698132932186\n",
      "Average loss at step  40000 :  4.290213817119598\n",
      "Nearest to 好:  , 宁, 酊, 与, 便, 受, 迩, 溶,\n",
      "Nearest to 何: 囗, 不,  , 款, 饼, 掾, 未, 猴,\n",
      "Nearest to 得: 取, 属, 睚, 设, 触, 个, 曜, 杀,\n",
      "Nearest to 情: 愁, 某, 觑, 窈, 侬, 肠, 筹, 会,\n",
      "Nearest to 江: 贼,  , 水, 截, 澌, 张, 鳊, 爷,\n",
      "Nearest to 来: 今, 去, 刺,  , 鷃, 犁, 也, 觉,\n",
      "Nearest to 声: 徵, 言, 史, 忔, 傲, 漏, 鸂, 费,\n",
      "Nearest to 香: 隔, 霜, 密, 这,  , 繇, 漏, 醑,\n",
      "Nearest to 流: 国, 阖, 劣, 渝,  , 叫, 钚, 东,\n",
      "Nearest to 愁: 情,  , 等, 懒, 嗔, 斟, 恨, 楝,\n",
      "Nearest to UNK: 柔, ■, 膝, 拘, 赠, 隘, 则, 跹,\n",
      "Nearest to 多: 籍, 噫, 时, 验, 磔, 缨, 及, 陋,\n",
      "Nearest to 清: 全, 舂,  , 急, 蛛, 孚, 於, 邂,\n",
      "Nearest to 。: ，, 、,  , ）, 鸪, 粳, 钝, 贬,\n",
      "Nearest to 似: 如, 营, 臆, 醴, 记, 舶, 动, 塑,\n",
      "Nearest to 如: 似, 染, 罕, 舫, 滁, 觉, 资, 浇,\n",
      "Average loss at step  42000 :  4.288083796977997\n",
      "Average loss at step  44000 :  4.334465644717216\n",
      "Average loss at step  46000 :  4.319810604929924\n",
      "Average loss at step  48000 :  4.328820533037185\n",
      "Average loss at step  50000 :  4.292075837492943\n",
      "Nearest to 好: 宁,  , 酊, 迩, 与, 便, 受, 身,\n",
      "Nearest to 何: 囗,  , 不, 掾, 款, 今, 饼, 恼,\n",
      "Nearest to 得: 取, 设, 睚, 能, 属, 与, 信, 了,\n",
      "Nearest to 情: 愁, 某, 窈, 觑, 筹, 肠, 裛, 侬,\n",
      "Nearest to 江: 贼,  , 水, 截, 澌, 鳊, 娅, 张,\n",
      "Nearest to 来: 今, 去, 刺, 也, 鷃,  , 觉, 又,\n",
      "Nearest to 声: 费, 徵, 鸣, 语, 鸂, 言, 傲, 么,\n",
      "Nearest to 香: 隔, 密, 霜,  , 磷, 醑, 繇, 鸯,\n",
      "Nearest to 流: 国, 阖, 劣,  , 渝, 东, 叫, 钚,\n",
      "Nearest to 愁:  , 情, 嗔, 恨, 等, 懒, 长, 躞,\n",
      "Nearest to UNK: 柔, ■, 膝, 拘, 聚, 赠, 迨, 镵,\n",
      "Nearest to 多: 籍, 噫, 时, 验, 陋, 磔, 及, 缨,\n",
      "Nearest to 清: 全, 舂,  , 急, 於, 瘗, 仇, 孚,\n",
      "Nearest to 。: ，, 、,  , ）, 鸪, 粳, 钝, 萨,\n",
      "Nearest to 似: 如, 营, 臆, 醴, 舶, 记, 怕, 塑,\n",
      "Nearest to 如: 似, 染, 浇, 罕, 舫, 资, 浓, 轺,\n",
      "Average loss at step  52000 :  4.318626224637032\n",
      "Average loss at step  54000 :  4.354956993937493\n",
      "Average loss at step  56000 :  4.310139630675316\n",
      "Average loss at step  58000 :  4.166949167370796\n",
      "Average loss at step  60000 :  4.188604158401489\n",
      "Nearest to 好: 宁, 酊,  , 迩, 与, 便, 春, 受,\n",
      "Nearest to 何: 不, 囗, 掾, 今, 饼, 款, 翛, 无,\n",
      "Nearest to 得: 取, 设, 著, 睚, 属, 能, 触, 杀,\n",
      "Nearest to 情: 愁, 某, 窈, 意, 些, 筹, 觑, 侬,\n",
      "Nearest to 江: 贼, 水,  , 澌, 截, 娅, 鳊, 挲,\n",
      "Nearest to 来: 今, 去, 刺, 犁, 教, 鷃, 也, 觉,\n",
      "Nearest to 声: 语, 鸣, 徵, 言, 鸂, 么, 傲, 忔,\n",
      "Nearest to 香: 隔, 密, 霜, 繇, 麟, 醑,  , 漏,\n",
      "Nearest to 流: 国, 阖, 劣, 渝, 叫,  , 东, 改,\n",
      "Nearest to 愁: 情,  , 嗔, 恨, 懒, 等, 躞, 长,\n",
      "Nearest to UNK: 柔, ■, 膝, 赠, 隘, 镵, 聚, C,\n",
      "Nearest to 多: 噫, 籍, 时, 验, 及, 陋, 磔, 缨,\n",
      "Nearest to 清: 全, 舂, 急, 仇, 邂, 孚,  , 瘗,\n",
      "Nearest to 。: ，, 、,  , ）, 萨, 鸪, 钝, 攻,\n",
      "Nearest to 似: 如, 营, 臆, 怕, 在, 舶, 塑, 撮,\n",
      "Nearest to 如: 似, 染, 罕, 资, 浓, 轺, 舫, 滁,\n",
      "Average loss at step  62000 :  4.199720952033997\n",
      "Average loss at step  64000 :  4.19909268027544\n",
      "Average loss at step  66000 :  4.1889122840166095\n",
      "Average loss at step  68000 :  4.203933172464371\n",
      "Average loss at step  70000 :  4.2174431371688845\n",
      "Nearest to 好: 宁, 酊, 迩,  , 受, 便, 玩, 蕤,\n",
      "Nearest to 何: 囗, 不, 掾, 今, 款, 皆, 无, 岂,\n",
      "Nearest to 得: 取, 睚, 设, 著, 属, 能, 个, 与,\n",
      "Nearest to 情: 愁, 某, 闷, 意, 肠, 会, 筹, 窈,\n",
      "Nearest to 江: 贼, 水,  , 娅, 截, 澌, 豆, 湘,\n",
      "Nearest to 来: 今, 去, 刺, 鷃, 教, 犁, 唤, 醺,\n",
      "Nearest to 声: 鸣, 语, 鸂, 么, 徵, 言, 耒, 紞,\n",
      "Nearest to 香: 隔, 密, 霜, 繇, 醑, 养, 麟, 鸯,\n",
      "Nearest to 流: 国, 阖, 劣, 渝, 东, 叫, 琤,  ,\n",
      "Nearest to 愁: 情,  , 恨, 嗔, 等, 躞, 懒, 怕,\n",
      "Nearest to UNK: 柔, ■, 隘, 膝, 镵, 聚, C, 赠,\n",
      "Nearest to 多: 噫, 籍, 时, 陋, 及, 验, 缨, 磔,\n",
      "Nearest to 清: 全, 舂, 急, 仇, 邂, 瘗,  , 秧,\n",
      "Nearest to 。: ，, 、,  , 鸪, ）, 萨, 贬, 钝,\n",
      "Nearest to 似: 如, 营, 怕, 臆, 舶, 唦, 是, 在,\n",
      "Nearest to 如: 似, 染, 罕, 舫, 枥, 巑, 浇, 滁,\n",
      "Average loss at step  72000 :  4.256755216360093\n",
      "Average loss at step  74000 :  4.252718309283257\n",
      "Average loss at step  76000 :  4.259496277213096\n",
      "Average loss at step  78000 :  4.231324171066285\n",
      "Average loss at step  80000 :  4.2511555979251865\n",
      "Nearest to 好: 宁, 酊, 便, 迩, 嘉, 受, 玩, 饶,\n",
      "Nearest to 何: 不, 掾, 今, 囗, 翛, 岂, 无, 踯,\n",
      "Nearest to 得: 取, 著, 睚, 设, 与, 属, 位, 能,\n",
      "Nearest to 情: 愁, 意, 某, 筹, 窈, 闷, 裛, 抨,\n",
      "Nearest to 江: 水, 贼, 截,  , 娅, 湘, 澌, 潮,\n",
      "Nearest to 来: 今, 去, 刺, 鷃, 教, 到, 又, 也,\n",
      "Nearest to 声: 鸣, 语, 徵, 言, 么, 鸂, 耒, 费,\n",
      "Nearest to 香: 隔, 密, 繇, 醑, 霜, 冰, 麟, 薰,\n",
      "Nearest to 流: 国, 阖, 渝, 劣, 叫, 琤, 莹, 东,\n",
      "Nearest to 愁: 情,  , 恨, 嗔, 躞, 等, 懒, 怕,\n",
      "Nearest to UNK: 柔, ■, 镵, C, 膝, 隘, 赠, E,\n",
      "Nearest to 多: 噫, 籍, 时, 陋, 验, 及, 磔, 唳,\n",
      "Nearest to 清: 全, 舂, 仇, 瘗, 秧, 邂, 釜, 榕,\n",
      "Nearest to 。: ，, 、,  , 鸪, 贬, ）, 粳, 萨,\n",
      "Nearest to 似: 如, 营, 臆, 怕, 舶, 是, 与, 便,\n",
      "Nearest to 如: 似, 染, 罕, 浓, 浇, 舫, 净, 枥,\n",
      "Average loss at step  82000 :  4.288894607543945\n",
      "Average loss at step  84000 :  4.235001082777977\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  86000 :  4.123196051001549\n",
      "Average loss at step  88000 :  4.1419100829362865\n",
      "Average loss at step  90000 :  4.139798870921135\n",
      "Nearest to 好: 宁, 酊, 嘉, 春, 迩, 便, 正, 玩,\n",
      "Nearest to 何: 不, 掾, 今, 无, 去, 深, 岂, 翛,\n",
      "Nearest to 得: 取, 著, 睚, 设, 位, 要, 属, 能,\n",
      "Nearest to 情: 愁, 意, 某, 闷, 筹, 会, 窈, 侬,\n",
      "Nearest to 江: 水, 贼, 娅, 截, 湘, 澌, 莆, 挲,\n",
      "Nearest to 来: 今, 去, 鷃, 刺, 教, 犁, 将, 到,\n",
      "Nearest to 声: 鸣, 语, 么, 鸂, 言, 徵, 耒, 悲,\n",
      "Nearest to 香: 隔, 密, 繇, 麟, 养, 霜, 醑, 愤,\n",
      "Nearest to 流: 国, 劣, 叫, 渝, 阖, 琤, 莹, 改,\n",
      "Nearest to 愁: 情, 恨,  , 嗔, 等, 躞, 怕, 懒,\n",
      "Nearest to UNK: 柔, 隘, C, ■, 赠, 忌, 镵, 聚,\n",
      "Nearest to 多: 噫, 籍, 陋, 时, 验, 及, 缨, 浓,\n",
      "Nearest to 清: 全, 急, 仇, 舂, 邂, 瘗, 秧, 冷,\n",
      "Nearest to 。: ，, 、, 鸪, ）, 粳,  , 钝, 贬,\n",
      "Nearest to 似: 如, 是, 怕, 在, 臆, 与, 撮, 负,\n",
      "Nearest to 如: 似, 染, 罕, 净, 随, 枥, 巑, 舫,\n",
      "Average loss at step  92000 :  4.145308611035347\n",
      "Average loss at step  94000 :  4.149204681694507\n",
      "Average loss at step  96000 :  4.155839713454246\n",
      "Average loss at step  98000 :  4.175552000641823\n",
      "Average loss at step  100000 :  4.207191764354706\n",
      "Nearest to 好: 宁, 酊, 春, 嘉, 预, 迩, 受, 玩,\n",
      "Nearest to 何: 掾, 不, 囗, 岂, 去, 否, 无, 今,\n",
      "Nearest to 得: 取, 著, 睚, 设, 个, 属, 与, 要,\n",
      "Nearest to 情: 愁, 意, 某, 闷, 筹, 会, 胙, 抨,\n",
      "Nearest to 江: 水, 贼, 截, 娅, 湘, 澌, 潮, 豆,\n",
      "Nearest to 来: 去, 今, 刺, 鷃, 犁, 教, 又, 伽,\n",
      "Nearest to 声: 鸣, 语, 鸂, 么, 徵, 耒, 言, 濠,\n",
      "Nearest to 香: 养, 宅, 密, 繇, 醑, 麟, 鸯, 隔,\n",
      "Nearest to 流: 国, 劣, 阖, 渝, 叫, 琤, 改, 莹,\n",
      "Nearest to 愁: 情, 恨, 嗔, 躞,  , 等, 怕, 遮,\n",
      "Nearest to UNK: 柔, C, ■, 隘, 忌, E, 镵, 纷,\n",
      "Nearest to 多: 噫, 籍, 陋, 时, 验, 及, 缨, 唳,\n",
      "Nearest to 清: 全, 仇, 舂, 秧, 瘗, 邂, 急, 釜,\n",
      "Nearest to 。: ，, 、, 鸪, ）, 贬, 粳, 耘, 钝,\n",
      "Nearest to 似: 如, 怕, 是, 在, 舶, 癖, 便, 臆,\n",
      "Nearest to 如: 似, 染, 罕, 净, 枥, 舫, 巑, 浇,\n",
      "Average loss at step  102000 :  4.204611418366432\n",
      "Average loss at step  104000 :  4.218393416523933\n",
      "Average loss at step  106000 :  4.191127658486367\n",
      "Average loss at step  108000 :  4.206404413580895\n",
      "Average loss at step  110000 :  4.243918975353241\n",
      "Nearest to 好: 宁, 酊, 预, 嘉, 迩, 饶, 便, 春,\n",
      "Nearest to 何: 不, 掾, 今, 岂, 去, 囗, 踯, 无,\n",
      "Nearest to 得: 取, 著, 睚, 与, 设, 要, 属, 能,\n",
      "Nearest to 情: 愁, 意, 某, 闷, 筹, 竦, 抨, 窈,\n",
      "Nearest to 江: 水, 贼, 湘, 截, 娅, 澌, 潮, 溪,\n",
      "Nearest to 来: 去, 今, 鷃, 到, 刺, 教, 犁, 又,\n",
      "Nearest to 声: 鸣, 语, 么, 徵, 鸂, 言, 悲, 濠,\n",
      "Nearest to 香: 密, 繇, 醑, 岱, 养, 麟, 宅, 爇,\n",
      "Nearest to 流: 国, 渝, 琤, 莹, 劣, 叫, 阖, 东,\n",
      "Nearest to 愁: 情, 恨, 嗔, 躞,  , 等, 怕, 长,\n",
      "Nearest to UNK: E, C, 忌, 隘, 柔, 纷, 毵, ■,\n",
      "Nearest to 多: 噫, 籍, 陋, 时, 验, 及, 崆, 槌,\n",
      "Nearest to 清: 全, 仇, 舂, 瘗, 秧, 邂, 冷, 釜,\n",
      "Nearest to 。: ，, 、, ）, 鸪, 贬, 粳, 萨,  ,\n",
      "Nearest to 似: 如, 是, 怕, 与, 舶, 负, 棚, 待,\n",
      "Nearest to 如: 似, 染, 罕, 于, 净, 枥, 巑, 浇,\n",
      "Average loss at step  112000 :  4.182999326944351\n",
      "Average loss at step  114000 :  4.097855582237243\n",
      "Average loss at step  116000 :  4.115758275747299\n",
      "Average loss at step  118000 :  4.101324941039086\n",
      "Average loss at step  120000 :  4.10508979666233\n",
      "Nearest to 好: 嘉, 宁, 酊, 预, 春, 迩, 饶, 玩,\n",
      "Nearest to 何: 不, 今, 掾, 岂, 无, 囗, 踯, 否,\n",
      "Nearest to 得: 取, 著, 睚, 属, 与, 设, 要, 了,\n",
      "Nearest to 情: 愁, 意, 某, 闷, 窈, 抨, 会, 竦,\n",
      "Nearest to 江: 水, 贼, 湘, 截, 娅, 湾, 沧, 澌,\n",
      "Nearest to 来: 去, 今, 鷃, 犁, 刺, 便, 教, 又,\n",
      "Nearest to 声: 语, 鸣, 言, 闻, 么, 悲, 听, 徵,\n",
      "Nearest to 香: 繇, 麟, 薰, 熏, 霜, 宅, 养, 岱,\n",
      "Nearest to 流: 琤, 劣, 叫, 渝, 莹, 乔, 改, 国,\n",
      "Nearest to 愁: 恨, 情, 嗔, 躞, 等, 怕, 廛, 泪,\n",
      "Nearest to UNK: C, E, 忌, 隘, 纷, ■, 柔, 锵,\n",
      "Nearest to 多: 噫, 籍, 陋, 及, 验, 时, 浓, 闲,\n",
      "Nearest to 清: 全, 仇, 舂, 秧, 邂, 瘗, 沸, 冷,\n",
      "Nearest to 。: ，, 、, 粳, 鸪, 萨, ）, 枨, 贬,\n",
      "Nearest to 似: 如, 是, 怕, 待, 与, 在, 舶, 负,\n",
      "Nearest to 如: 似, 染, 于, 净, 枥, 随, 罕, 舫,\n",
      "Average loss at step  122000 :  4.124811386883259\n",
      "Average loss at step  124000 :  4.118149163126946\n",
      "Average loss at step  126000 :  4.1492741411924365\n",
      "Average loss at step  128000 :  4.175064274907112\n",
      "Average loss at step  130000 :  4.168147477269173\n",
      "Nearest to 好: 预, 酊, 嘉, 宁, 饶, 迩, 春, 玩,\n",
      "Nearest to 何: 掾, 不, 囗, 岂, 踯, 今, 无, 、,\n",
      "Nearest to 得: 取, 著, 睚, 个, 要, 与, 属, 能,\n",
      "Nearest to 情: 愁, 意, 竦, 抨, 闷, 某, 筹, 胙,\n",
      "Nearest to 江: 水, 截, 贼, 娅, 湘, 豆, 沧, 湾,\n",
      "Nearest to 来: 去, 今, 刺, 鷃, 又, 到, 便, 教,\n",
      "Nearest to 声: 鸣, 语, 听, 濠, 悲, 闻, 鸂, 耒,\n",
      "Nearest to 香: 养, 宅, 岱, 醑, 熏, 繇, 麟, 爇,\n",
      "Nearest to 流: 琤, 莹, 劣, 叫, 渝, 改, 乔, 国,\n",
      "Nearest to 愁: 恨, 情, 嗔, 躞, 廛, 秋, 怕, 遮,\n",
      "Nearest to UNK: C, E, 忌, 醉, 隘, |, 纷, 毵,\n",
      "Nearest to 多: 噫, 籍, 陋, 时, 验, 遂, 浓, 及,\n",
      "Nearest to 清: 全, 仇, 舂, 瘗, 邂, 秧, 冷, 湖,\n",
      "Nearest to 。: ，, 、, ）, 鸪, 粳, 楷, 耘, 贬,\n",
      "Nearest to 似: 如, 怕, 是, 待, 棚, 舶, 癖, 与,\n",
      "Nearest to 如: 似, 染, 于, 净, 巑, 枥, 浇, 罕,\n",
      "Average loss at step  132000 :  4.189893383979797\n",
      "Average loss at step  134000 :  4.164245716571807\n",
      "Average loss at step  136000 :  4.171899649858474\n",
      "Average loss at step  138000 :  4.215382757663726\n",
      "Average loss at step  140000 :  4.144414987325669\n",
      "Nearest to 好: 预, 嘉, 宁, 饶, 酊, 迩, 玩, 值,\n",
      "Nearest to 何: 岂, 无, 掾, 今, 踯, 那, 不, 翛,\n",
      "Nearest to 得: 取, 著, 要, 与, 能, 睚, 了, 属,\n",
      "Nearest to 情: 愁, 意, 某, 心, 抨, 窈, 闷, 恨,\n",
      "Nearest to 江: 水, 湘, 贼, 溪, 湾, 截, 娅, 湖,\n",
      "Nearest to 来: 去, 今, 鷃, 刺, 又, 犁, 将, 懒,\n",
      "Nearest to 声: 鸣, 语, 听, 闻, 鼓, 濠, 鸂, 徵,\n",
      "Nearest to 香: 醑, 岱, 麟, 密, 薰, 宅, 繇, 养,\n",
      "Nearest to 流: 琤, 莹, 渝, 叫, 国, 劣, 改, 乔,\n",
      "Nearest to 愁: 恨, 情, 嗔, 躞, 怕, 廛, 等, 谩,\n",
      "Nearest to UNK: C, 忌, E, 隘, |, 醉, 毵, 纷,\n",
      "Nearest to 多: 噫, 籍, 陋, 时, 验, 人, 浓, 及,\n",
      "Nearest to 清: 仇, 全, 瘗, 秧, 舂, 月, 精, 冷,\n",
      "Nearest to 。: ，, 、, ）, 粳, 缙, （, 萨, 涔,\n",
      "Nearest to 似: 如, 是, 怕, 待, 奈, 与, 负, 舶,\n",
      "Nearest to 如: 似, 染, 于, 巑, 罕, 净, 枥, 舫,\n",
      "Average loss at step  142000 :  4.0723957463502884\n",
      "Average loss at step  144000 :  4.095391265273094\n",
      "Average loss at step  146000 :  4.077530187726021\n",
      "Average loss at step  148000 :  4.078762555122376\n",
      "Average loss at step  150000 :  4.103729054510594\n",
      "Nearest to 好: 嘉, 预, 饶, 佳, 酊, 春, 宁, □,\n",
      "Nearest to 何: 不, □, 岂, 今, 掾, 踯, 无, 否,\n",
      "Nearest to 得: 取, 著, 与, 个, 属, 设, 要, 了,\n",
      "Nearest to 情: 意, 愁, □, 某, 窈, 心, 抨, 胙,\n",
      "Nearest to 江: 水, 贼, 湘, 湾, 娅, 截, 溪, 湖,\n",
      "Nearest to 来: 去, 今, 鷃, 懒, 便, 犁, 刺, 伽,\n",
      "Nearest to 声: 鸣, 语, 闻, 听, 悲, 鸂, 耒, 濠,\n",
      "Nearest to 香: 熏, 薰, 麟, 养, 宅, 眷, 繇, 霜,\n",
      "Nearest to 流: 莹, 琤, 叫, 乔, 渝, 改, 劣, □,\n",
      "Nearest to 愁: 恨, 情, 嗔, 躞, 怕, 等, 廛, 悄,\n",
      "Nearest to UNK: 忌, C, E, 醉, 隘, |, 毵, 纷,\n",
      "Nearest to 多: 噫, 陋, 籍, 验, 浓, 时, 及, □,\n",
      "Nearest to 清: 仇, 秧, 邂, 舂, 全, 沸, 瘗, □,\n",
      "Nearest to 。: ，, 、, 鸪, □, 粳, ）, 萨, 聂,\n",
      "Nearest to 似: 如, 是, 怕, 奈, 与, 在, 待, 负,\n",
      "Nearest to 如: 似, 染, 于, 巑, 枥, 净, 舫, 随,\n",
      "Average loss at step  152000 :  4.0882221300601955\n",
      "Average loss at step  154000 :  4.120785082101822\n",
      "Average loss at step  156000 :  4.148717898607254\n",
      "Average loss at step  158000 :  4.146971092820167\n",
      "Average loss at step  160000 :  4.1633086167573925\n",
      "Nearest to 好: 预, 嘉, 饶, 酊, 佳, 宁, 迩, 麋,\n",
      "Nearest to 何: 岂, 踯, 无, 囗, 掾, 不, 否, 去,\n",
      "Nearest to 得: 取, 著, 与, 要, 睚, 个, 属, 设,\n",
      "Nearest to 情: 愁, 意, 抨, 竦, 心, 闷, 恨, 胙,\n",
      "Nearest to 江: 水, 截, 湾, 娅, 淮, 贼, 湘, 溪,\n",
      "Nearest to 来: 去, 鷃, 今, 又, 刺, 懒, 犁, 便,\n",
      "Nearest to 声: 鸣, 语, 听, 闻, 濠, 悲, 么, 徵,\n",
      "Nearest to 香: 熏, 养, 醑, 岱, 薰, 芳, 宅, 麟,\n",
      "Nearest to 流: 琤, 莹, 渝, 叫, 乔, 劣, 改, 秋,\n",
      "Nearest to 愁: 恨, 情, 嗔, 躞, 秋, 廛, 怕, 等,\n",
      "Nearest to UNK: 醉, C, 忌, E, |, 毵, 纷, 隘,\n",
      "Nearest to 多: 噫, 籍, 陋, 浓, 时, 验, 遂, 帡,\n",
      "Nearest to 清: 全, 仇, 瘗, 秧, 冷, 邂, 榕, 精,\n",
      "Nearest to 。: ，, 、, ）, 鸪, 粳, □, 贬, 萨,\n",
      "Nearest to 似: 如, 怕, 是, 与, 奈, 待, 癖, 共,\n",
      "Nearest to 如: 似, 枥, 于, 染, 巑, 净, 浇, 肓,\n",
      "Average loss at step  162000 :  4.143426740527153\n",
      "Average loss at step  164000 :  4.150845461726188\n",
      "Average loss at step  166000 :  4.181087824106217\n",
      "Average loss at step  168000 :  4.124335498571396\n",
      "Average loss at step  170000 :  4.048858199715614\n",
      "Nearest to 好: 嘉, 饶, 佳, 预, 酊, 宁, 宜, 值,\n",
      "Nearest to 何: 无, 不, 岂, 踯, 掾, 翛, 去, 否,\n",
      "Nearest to 得: 取, 著, 要, 了, 与, 属, 睚, 个,\n",
      "Nearest to 情: 意, 愁, 某, 恨, 心, 抨, 胙, 竦,\n",
      "Nearest to 江: 水, 溪, 湘, 湾, 淮, 湖, 娅, 贼,\n",
      "Nearest to 来: 今, 去, 鷃, 懒, 刺, 又, 犁, 伽,\n",
      "Nearest to 声: 鸣, 语, 闻, 听, 濠, 悲, 徵, 鸂,\n",
      "Nearest to 香: 薰, 熏, 麟, 芳, 繇, 宅, 烟, 躞,\n",
      "Nearest to 流: 渝, 琤, 莹, 叫, 改, 乔, 劣, 漙,\n",
      "Nearest to 愁: 恨, 情, 嗔, 躞, 怕, 心, 秋, 等,\n",
      "Nearest to UNK: C, 忌, E, |, 醉, 毵, 隘, 纷,\n",
      "Nearest to 多: 噫, 浓, 籍, 陋, 验, 人, 衷, 时,\n",
      "Nearest to 清: 仇, 瘗, 秧, 舂, 沸, 邂, 精, 全,\n",
      "Nearest to 。: ，, 、, 萨, 缙, ）, 粳, 鸪, 糠,\n",
      "Nearest to 似: 如, 是, 怕, 奈, 待, 记, 负, 与,\n",
      "Nearest to 如: 似, 于, 染, 净, 枥, 巑, 随, 而,\n",
      "Average loss at step  172000 :  4.08794732272625\n",
      "Average loss at step  174000 :  4.04599348282814\n",
      "Average loss at step  176000 :  4.063958912372589\n",
      "Average loss at step  178000 :  4.087082321047783\n",
      "Average loss at step  180000 :  4.0695731687545775\n",
      "Nearest to 好: 嘉, 佳, 预, 饶, 春, 酊, 娅, 喜,\n",
      "Nearest to 何: 岂, 不, 无, 掾, 踯, 否, 镞, 囗,\n",
      "Nearest to 得: 取, 著, 个, 要, 与, 了, 属, 睚,\n",
      "Nearest to 情: 意, 愁, 恨, 抨, 竦, 会, 胙, 心,\n",
      "Nearest to 江: 水, 溪, 湾, 淮, 娅, 湘, 截, 贼,\n",
      "Nearest to 来: 今, 去, 鷃, 懒, 伽, 便, 犁, 欤,\n",
      "Nearest to 声: 鸣, 语, 听, 闻, 悲, 濠, 笛, 鸂,\n",
      "Nearest to 香: 熏, 薰, 宅, 麟, 养, 愤, 躞, 眷,\n",
      "Nearest to 流: 莹, 渝, 琤, 乔, 叫, 改, 劣, 掀,\n",
      "Nearest to 愁: 恨, 情, 嗔, 怕, 忧, 躞, 等, 廛,\n",
      "Nearest to UNK: 忌, C, E, |, 醉, 毵, 隘, 纷,\n",
      "Nearest to 多: 噫, 浓, 籍, 陋, 验, 添, 时, 崆,\n",
      "Nearest to 清: 秧, 仇, 瘗, 冷, 舂, 沸, 全, 升,\n",
      "Nearest to 。: ，, 、, ）, 鸪, 枨, 粳, 到, 攽,\n",
      "Nearest to 似: 如, 奈, 怕, 是, 记, 癖, 舶, 棚,\n",
      "Nearest to 如: 似, 枥, 巑, 谬, 随, 于, 净, 染,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  182000 :  4.097173017024994\n",
      "Average loss at step  184000 :  4.141375746011734\n",
      "Average loss at step  186000 :  4.12637870657444\n",
      "Average loss at step  188000 :  4.143295730471611\n",
      "Average loss at step  190000 :  4.120061821103096\n",
      "Nearest to 好: 嘉, 饶, 佳, 预, 酊, 乐, 娅, 喜,\n",
      "Nearest to 何: 踯, 无, 掾, 岂, 否, 底, 到, 囗,\n",
      "Nearest to 得: 取, 著, 与, 要, 个, 了, 睚, 设,\n",
      "Nearest to 情: 愁, 意, 竦, 抨, 恨, 心, 胙, 某,\n",
      "Nearest to 江: 水, 溪, 淮, 湾, 湖, 湘, 截, 娅,\n",
      "Nearest to 来: 去, 鷃, 今, 懒, 又, 刺, 到, 欤,\n",
      "Nearest to 声: 鸣, 语, 听, 闻, 濠, 悲, 笛, 鼓,\n",
      "Nearest to 香: 熏, 芳, 薰, 宅, 醑, 麟, 养, 躞,\n",
      "Nearest to 流: 莹, 琤, 渝, 叫, 乔, 掀, 改, 劣,\n",
      "Nearest to 愁: 恨, 情, 嗔, 躞, 忧, 秋, 廛, 怕,\n",
      "Nearest to UNK: E, 忌, C, 醉, |, 毵, 纷, 谢,\n",
      "Nearest to 多: 噫, 浓, 籍, 陋, 验, 帡, 时, 槌,\n",
      "Nearest to 清: 仇, 秧, 全, 瘗, 精, 璋, 馨, 榕,\n",
      "Nearest to 。: ，, 、, ）, 萨, 粳, 鸪, （, 嫌,\n",
      "Nearest to 似: 如, 奈, 是, 怕, 棚, 记, 待, 负,\n",
      "Nearest to 如: 似, 枥, 于, 净, 肓, 而, 巑, 谬,\n",
      "Average loss at step  192000 :  4.136732415437699\n",
      "Average loss at step  194000 :  4.163244841933251\n",
      "Average loss at step  196000 :  4.095155725955963\n",
      "Average loss at step  198000 :  4.028839223384857\n",
      "Average loss at step  200000 :  4.083588879585266\n",
      "Nearest to 好: 嘉, 佳, 预, 饶, 娅, 春, 宁, 酊,\n",
      "Nearest to 何: 不, 岂, 踯, 否, 那, 掾, 翛, 镞,\n",
      "Nearest to 得: 取, 著, 了, 要, 个, 与, 属, 设,\n",
      "Nearest to 情: 意, 愁, 心, 恨, 抨, 竦, 某, 窈,\n",
      "Nearest to 江: 水, 淮, 湾, 湘, 溪, 海, 娅, 湖,\n",
      "Nearest to 来: 去, 懒, 鷃, 今, 刺, 犁, 伽, 便,\n",
      "Nearest to 声: 鸣, 语, 闻, 听, 濠, 悲, 笛, 距,\n",
      "Nearest to 香: 熏, 薰, 麟, 芳, 愤, 繇, 养, 宅,\n",
      "Nearest to 流: 琤, 莹, 渝, 乔, 叫, 改, 掀, 祇,\n",
      "Nearest to 愁: 恨, 情, 嗔, 躞, 怕, 忧, 心, 怨,\n",
      "Nearest to UNK: C, 忌, E, |, 谢, 醉, 毵, 隘,\n",
      "Nearest to 多: 噫, 浓, 陋, 籍, 验, 崆, 帡, 衷,\n",
      "Nearest to 清: 仇, 沸, 秧, 冷, 瘗, 榕, 精, 舂,\n",
      "Nearest to 。: ，, 、, 萨, ）, 粳, 枨, 贬, 楷,\n",
      "Nearest to 似: 如, 奈, 是, 怕, 记, 待, 在, 为,\n",
      "Nearest to 如: 似, 于, 净, 染, 枥, 巑, 奈, 随,\n",
      "Average loss at step  202000 :  4.021061260342598\n",
      "Average loss at step  204000 :  4.0523093889951705\n",
      "Average loss at step  206000 :  4.0665238421559335\n",
      "Average loss at step  208000 :  4.049928915858269\n",
      "Average loss at step  210000 :  4.079139105916023\n",
      "Nearest to 好: 嘉, 佳, 饶, 预, 娅, 春, 喜, 酊,\n",
      "Nearest to 何: 掾, 岂, 无, 镞, 不, 踯, 那, 否,\n",
      "Nearest to 得: 取, 著, 个, 与, 要, 了, 属, 设,\n",
      "Nearest to 情: 意, 愁, 抨, 恨, 竦, 心, 胙, 少,\n",
      "Nearest to 江: 水, 淮, 湾, 溪, 娅, 湘, 湖, 贼,\n",
      "Nearest to 来: 去, 今, 鷃, 便, 懒, 又, 伽, 欤,\n",
      "Nearest to 声: 鸣, 语, 闻, 听, 笛, 濠, 悲, 鼓,\n",
      "Nearest to 香: 熏, 薰, 芳, 麟, 醑, 烟, 宅, 躞,\n",
      "Nearest to 流: 琤, 莹, 渝, 掀, 乔, 改, 叫, 劣,\n",
      "Nearest to 愁: 恨, 情, 嗔, 忧, 躞, 怕, 感, 廛,\n",
      "Nearest to UNK: 忌, E, C, 醉, |, 毵, 纷, 谢,\n",
      "Nearest to 多: 噫, 浓, 陋, 籍, 添, 遂, 时, 验,\n",
      "Nearest to 清: 秧, 仇, 冷, 沸, 舂, 瘗, 全, 渊,\n",
      "Nearest to 。: ，, 、, ）, 粳, 鸪, 萨, ；, 缙,\n",
      "Nearest to 似: 如, 是, 奈, 怕, 记, 共, 待, 癖,\n",
      "Nearest to 如: 似, 巑, 于, 枥, 谬, 肓, 逊, 而,\n",
      "Average loss at step  212000 :  4.131860385775566\n",
      "Average loss at step  214000 :  4.109016810774803\n",
      "Average loss at step  216000 :  4.1233746769428254\n",
      "Average loss at step  218000 :  4.102423210501671\n",
      "Average loss at step  220000 :  4.120553683280945\n",
      "Nearest to 好: 饶, 佳, 嘉, 预, 乐, 喜, 娅, 酊,\n",
      "Nearest to 何: 踯, 岂, 掾, 不, 否, 无, 那, 镞,\n",
      "Nearest to 得: 取, 著, 与, 要, 个, 了, 属, 睚,\n",
      "Nearest to 情: 意, 愁, 恨, 竦, 抨, 心, 伤, 胙,\n",
      "Nearest to 江: 水, 淮, 湾, 溪, 湘, 截, 娅, 廿,\n",
      "Nearest to 来: 去, 懒, 鷃, 今, 又, 嚲, 刺, 欤,\n",
      "Nearest to 声: 鸣, 语, 闻, 听, 濠, 悲, 笛, 距,\n",
      "Nearest to 香: 熏, 薰, 芳, 醑, 愤, 麟, 眷, 爬,\n",
      "Nearest to 流: 莹, 渝, 掀, 琤, 乔, 祇, 叫, 改,\n",
      "Nearest to 愁: 恨, 情, 嗔, 忧, 躞, 怨, 感, 怕,\n",
      "Nearest to UNK: 忌, C, E, 谢, 醉, |, 崇, 毵,\n",
      "Nearest to 多: 噫, 浓, 籍, 陋, 人, 帡, 验, 添,\n",
      "Nearest to 清: 仇, 秧, 沸, 瘗, 邂, 渊, 精, 璋,\n",
      "Nearest to 。: ，, 、, 鸪, ）, ；, 7, 之, 贬,\n",
      "Nearest to 似: 如, 奈, 是, 记, 待, 怕, 共, 与,\n",
      "Nearest to 如: 似, 枥, 于, 肓, 巑, 随, 砚, 浇,\n",
      "Average loss at step  222000 :  4.144488776683807\n",
      "Average loss at step  224000 :  4.068629339575767\n",
      "Average loss at step  226000 :  4.021874459862709\n",
      "Average loss at step  228000 :  4.064733155965805\n",
      "Average loss at step  230000 :  4.012480271577835\n",
      "Nearest to 好: 佳, 嘉, 饶, 预, 娅, 春, 乐, 喜,\n",
      "Nearest to 何: 无, 踯, 岂, 不, 忭, 镞, 那, 今,\n",
      "Nearest to 得: 取, 著, 了, 个, 与, 要, 见, 设,\n",
      "Nearest to 情: 意, 愁, 恨, 抨, 心, 竦, 某, 名,\n",
      "Nearest to 江: 水, 淮, 湾, 溪, 湘, 娅, 廿, 海,\n",
      "Nearest to 来: 去, 懒, 鷃, 今, 奈, 犁, 欤, 便,\n",
      "Nearest to 声: 语, 鸣, 听, 闻, 笛, 阕, 濠, 悲,\n",
      "Nearest to 香: 熏, 薰, 芳, 宅, 繇, 愤, 麟, 爬,\n",
      "Nearest to 流: 琤, 莹, 乔, 渝, 掀, 叫, 改, 祇,\n",
      "Nearest to 愁: 恨, 情, 嗔, 忧, 心, 躞, 怕, 怨,\n",
      "Nearest to UNK: 忌, C, E, 谢, |, 崇, 醉, 掾,\n",
      "Nearest to 多: 噫, 浓, 陋, 籍, 崆, 帡, 址, 添,\n",
      "Nearest to 清: 沸, 秧, 仇, 冷, 瘗, 一, 精, 月,\n",
      "Nearest to 。: ，, 、, 萨, ）, 粳, 糠, 菌, 枨,\n",
      "Nearest to 似: 如, 是, 奈, 共, 怕, 在, 记, 与,\n",
      "Nearest to 如: 似, 枥, 净, 于, 随, 巑, 漱, 肓,\n",
      "Average loss at step  232000 :  4.041102538585663\n",
      "Average loss at step  234000 :  4.05248662930727\n",
      "Average loss at step  236000 :  4.0366055957078935\n",
      "Average loss at step  238000 :  4.065770978927612\n",
      "Average loss at step  240000 :  4.125371764302254\n",
      "Nearest to 好: 佳, 饶, 嘉, 预, 娅, 乐, 酊, 麋,\n",
      "Nearest to 何: 踯, 岂, 镞, 囗, 忭, 掾, 否, 无,\n",
      "Nearest to 得: 取, 著, 个, 要, 与, 了, 睚, 怕,\n",
      "Nearest to 情: 意, 愁, 恨, 抨, 竦, 心, 少, 胙,\n",
      "Nearest to 江: 水, 淮, 湾, 溪, 廿, 湘, 截, 娅,\n",
      "Nearest to 来: 去, 鷃, 今, 懒, 煎, 奈, 嚲, 驭,\n",
      "Nearest to 声: 鸣, 语, 听, 闻, 濠, 笛, 鼓, 悲,\n",
      "Nearest to 香: 熏, 薰, 芳, 宅, 眷, 醑, 躞, 岱,\n",
      "Nearest to 流: 琤, 掀, 莹, 乔, 渝, 叫, 改, 祇,\n",
      "Nearest to 愁: 恨, 情, 嗔, 忧, 躞, 秋, 怕, 怨,\n",
      "Nearest to UNK: 忌, E, C, |, 毵, 崇, 醉, 掾,\n",
      "Nearest to 多: 噫, 浓, 陋, 籍, 帡, 深, >, 肾,\n",
      "Nearest to 清: 秧, 仇, 渊, 舂, 沸, 瘗, 璋, 邂,\n",
      "Nearest to 。: ，, 、, ）, 獠, 查, 3, 鸪, 楷,\n",
      "Nearest to 似: 如, 奈, 是, 癖, 在, 共, 怕, 记,\n",
      "Nearest to 如: 似, 肓, 谬, 于, 枥, 巑, 逊, 浇,\n",
      "Average loss at step  242000 :  4.096717724323272\n",
      "Average loss at step  244000 :  4.104770314931869\n",
      "Average loss at step  246000 :  4.091084091186524\n",
      "Average loss at step  248000 :  4.111395114421844\n",
      "Average loss at step  250000 :  4.125282636404037\n",
      "Nearest to 好: 饶, 佳, 嘉, 预, 娅, 乐, 麋, 酊,\n",
      "Nearest to 何: 踯, 岂, 那, 不, 无, 忭, 镞, 底,\n",
      "Nearest to 得: 取, 著, 要, 与, 了, 个, 睚, 见,\n",
      "Nearest to 情: 意, 愁, 恨, 竦, 抨, 心, 思, 少,\n",
      "Nearest to 江: 水, 淮, 溪, 湾, 廿, 湘, 海, 娅,\n",
      "Nearest to 来: 去, 鷃, 今, 欤, 懒, 又, 奈, 驭,\n",
      "Nearest to 声: 鸣, 语, 闻, 听, 濠, 笛, 悲, 鼓,\n",
      "Nearest to 香: 熏, 芳, 薰, 醑, 宅, 爬, 眷, 躞,\n",
      "Nearest to 流: 琤, 莹, 掀, 渝, 乔, 叫, 祇, 改,\n",
      "Nearest to 愁: 恨, 情, 嗔, 忧, 躞, 怨, 怕, 心,\n",
      "Nearest to UNK: 忌, C, E, |, 崇, 毵, 谢, 醉,\n",
      "Nearest to 多: 噫, 浓, 籍, 陋, 帡, 人, 屐, 验,\n",
      "Nearest to 清: 仇, 璋, 渊, 秧, 瘗, 沸, 朔, 精,\n",
      "Nearest to 。: ，, 、, ）, 鸪, 粳, 诳, 萨, 耘,\n",
      "Nearest to 似: 如, 是, 奈, 羡, 怕, 见, 记, 待,\n",
      "Nearest to 如: 似, 于, 逊, 浇, 枥, 巑, 肓, 底,\n",
      "Average loss at step  252000 :  4.048529764890671\n",
      "Average loss at step  254000 :  4.016868611574173\n",
      "Average loss at step  256000 :  4.0544270606040955\n",
      "Average loss at step  258000 :  4.003917886018753\n",
      "Average loss at step  260000 :  4.021977302253246\n",
      "Nearest to 好: 佳, 嘉, 饶, 乐, 预, 娅, 春, 喜,\n",
      "Nearest to 何: 不, 踯, 镞, 岂, 那, 否, 忭, 无,\n",
      "Nearest to 得: 取, 著, 个, 与, 了, 见, 要, 设,\n",
      "Nearest to 情: 意, 愁, 恨, 抨, 竦, 心, 少, 名,\n",
      "Nearest to 江: 淮, 水, 湾, 溪, 湘, 廿, 湖, 截,\n",
      "Nearest to 来: 去, 鷃, 懒, 今, 又, 嚲, 奈, 欤,\n",
      "Nearest to 声: 语, 鸣, 听, 闻, 阕, 笛, 悲, 距,\n",
      "Nearest to 香: 熏, 薰, 芳, 愤, 爬, 宅, 雹, 躞,\n",
      "Nearest to 流: 琤, 掀, 莹, 渝, 乔, 叫, 祇, 改,\n",
      "Nearest to 愁: 恨, 嗔, 情, 忧, 怕, 泪, 躞, 怨,\n",
      "Nearest to UNK: 忌, E, C, |, 谢, ＿, 崇, 掾,\n",
      "Nearest to 多: 噫, 浓, 籍, 陋, 深, 帡, 址, >,\n",
      "Nearest to 清: 秧, 沸, 仇, 一, 璋, 舂, 瘗, 渊,\n",
      "Nearest to 。: ，, 、, 粳, ）, 鸪, ；, 悚, 庑,\n",
      "Nearest to 似: 如, 奈, 是, 羡, 在, 共, 怕, 与,\n",
      "Nearest to 如: 似, 逊, 枥, 巑, 于, 谬, 肓, 底,\n",
      "Average loss at step  262000 :  4.038219729542733\n",
      "Average loss at step  264000 :  4.028902943611145\n",
      "Average loss at step  266000 :  4.058936419844628\n",
      "Average loss at step  268000 :  4.109740988850594\n",
      "Average loss at step  270000 :  4.07849491238594\n",
      "Nearest to 好: 佳, 饶, 嘉, 预, 娅, 乐, 麋, 慎,\n",
      "Nearest to 何: 踯, 镞, 岂, 那, 囗, 忭, 掾, 多,\n",
      "Nearest to 得: 取, 著, 个, 要, 了, 与, 怕, 睚,\n",
      "Nearest to 情: 意, 愁, 恨, 竦, 抨, 胙, 少, 心,\n",
      "Nearest to 江: 水, 淮, 湘, 溪, 廿, 湾, 截, 湖,\n",
      "Nearest to 来: 去, 鷃, 懒, 又, 驭, 嚲, 今, 奈,\n",
      "Nearest to 声: 语, 鸣, 听, 闻, 笛, 濠, 悲, 距,\n",
      "Nearest to 香: 熏, 芳, 薰, 宅, 躞, 眷, 霜, 爬,\n",
      "Nearest to 流: 莹, 琤, 掀, 渝, 叫, 乔, 祇, 摇,\n",
      "Nearest to 愁: 恨, 情, 嗔, 忧, 躞, 秋, 怕, 怨,\n",
      "Nearest to UNK: 忌, E, C, 醉, |, 倒, 崇, ＿,\n",
      "Nearest to 多: 噫, 浓, 籍, 陋, 深, >, 肾, 帡,\n",
      "Nearest to 清: 渊, 仇, 秧, 沸, 璋, 邂, 瘗, 冷,\n",
      "Nearest to 。: ，, 、, ）, 萨, 涔, 鸪, ；, 糠,\n",
      "Nearest to 似: 如, 奈, 是, 羡, 记, 癖, 共, 待,\n",
      "Nearest to 如: 似, 憧, 逊, 于, 巑, 肓, 枥, 砚,\n",
      "Average loss at step  272000 :  4.094812011837959\n",
      "Average loss at step  274000 :  4.080562819242477\n",
      "Average loss at step  276000 :  4.102313595533371\n",
      "Average loss at step  278000 :  4.110320178508759\n",
      "Average loss at step  280000 :  4.026908023357391\n",
      "Nearest to 好: 佳, 饶, 嘉, 预, 娅, 慎, 乐, 巧,\n",
      "Nearest to 何: 踯, 无, 岂, 那, 镞, 多, 去, 否,\n",
      "Nearest to 得: 取, 著, 了, 个, 要, 怕, 与, 疗,\n",
      "Nearest to 情: 意, 愁, 恨, 抨, 心, 思, 竦, 少,\n",
      "Nearest to 江: 淮, 水, 溪, 湾, 湘, 湖, 廿, 海,\n",
      "Nearest to 来: 去, 鷃, 懒, 今, 又, 欤, 嚲, 刺,\n",
      "Nearest to 声: 鸣, 语, 闻, 听, 笛, 阕, 濠, 距,\n",
      "Nearest to 香: 熏, 薰, 芳, 醑, 麟, 爬, 药, 衾,\n",
      "Nearest to 流: 莹, 琤, 掀, 渝, 祇, 叫, 廉, 乔,\n",
      "Nearest to 愁: 恨, 情, 嗔, 忧, 怨, 心, 怕, 、,\n",
      "Nearest to UNK: 忌, C, E, 谢, 崇, |, 舵, 毵,\n",
      "Nearest to 多: 噫, 浓, 人, 籍, 何, 贫, 陋, 帡,\n",
      "Nearest to 清: 仇, 沸, 精, 秧, 渊, 朔, 璋, 月,\n",
      "Nearest to 。: ，, 、, ）, 花, 粳, 糠, 涔, 萨,\n",
      "Nearest to 似: 如, 奈, 是, 记, 羡, 见, 共, 怕,\n",
      "Nearest to 如: 似, 于, 砚, 逊, 奈, 巑, 枥, 肓,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  282000 :  4.019594614863395\n",
      "Average loss at step  284000 :  4.038931178689003\n",
      "Average loss at step  286000 :  3.99467098903656\n",
      "Average loss at step  288000 :  4.011116876244545\n",
      "Average loss at step  290000 :  4.031240828096867\n",
      "Nearest to 好: 佳, 饶, 嘉, 乐, 预, 娅, 慎, 春,\n",
      "Nearest to 何: 镞, 不, 踯, 岂, 那, 否, 无, 忭,\n",
      "Nearest to 得: 取, 著, 个, 了, 见, 与, 怕, 要,\n",
      "Nearest to 情: 意, 愁, 恨, 抨, 竦, 诉, 胙, 少,\n",
      "Nearest to 江: 淮, 水, 湾, 溪, 湘, 廿, 湖, 潇,\n",
      "Nearest to 来: 去, 鷃, 懒, 嚲, 今, 驭, 欤, 奈,\n",
      "Nearest to 声: 语, 鸣, 闻, 听, 笛, 阕, 叫, 濠,\n",
      "Nearest to 香: 熏, 薰, 眷, 第, 宅, 芳, 爬, 麟,\n",
      "Nearest to 流: 琤, 掀, 莹, 乔, 渝, 祇, 叫, 廉,\n",
      "Nearest to 愁: 恨, 情, 嗔, 忧, 躞, 怨, 怕, 凄,\n",
      "Nearest to UNK: 忌, E, C, 崇, |, 舵, 谢, 丙,\n",
      "Nearest to 多: 噫, 浓, 陋, 籍, 深, 帡, 何, >,\n",
      "Nearest to 清: 秧, 仇, 沸, 朔, 邂, 渊, 懔, 璋,\n",
      "Nearest to 。: 、, ，, ）, ；, 鸪, 粳, 萨, 淘,\n",
      "Nearest to 似: 如, 奈, 是, 羡, 共, 怕, 记, 在,\n",
      "Nearest to 如: 似, 枥, 逊, 巑, 漱, 于, 憧, 随,\n",
      "Average loss at step  292000 :  4.020355325698852\n",
      "Average loss at step  294000 :  4.056507387757302\n",
      "Average loss at step  296000 :  4.097770983099937\n",
      "Average loss at step  298000 :  4.0694929336309436\n",
      "Average loss at step  300000 :  4.088323454499244\n",
      "Nearest to 好: 佳, 饶, 乐, 预, 嘉, 慎, 娅, 麋,\n",
      "Nearest to 何: 踯, 岂, 那, 镞, 否, 底, 忭, 多,\n",
      "Nearest to 得: 取, 著, 个, 与, 怕, 了, 要, 旋,\n",
      "Nearest to 情: 意, 愁, 恨, 抨, 竦, 心, 胙, 思,\n",
      "Nearest to 江: 淮, 水, 溪, 廿, 湾, 截, 湘, 潇,\n",
      "Nearest to 来: 去, 鷃, 懒, 又, 嚲, 归, 驭, 今,\n",
      "Nearest to 声: 语, 鸣, 闻, 听, 濠, 笛, 阕, 距,\n",
      "Nearest to 香: 熏, 薰, 芳, 醑, 眷, 宅, 凉, 躞,\n",
      "Nearest to 流: 掀, 琤, 莹, 渝, 乔, 祇, 姑, 叫,\n",
      "Nearest to 愁: 恨, 情, 嗔, 忧, 怨, 秋, 躞, 怕,\n",
      "Nearest to UNK: 忌, C, E, 舵, 醉, ＿, 崇, 透,\n",
      "Nearest to 多: 噫, 浓, 深, 籍, 陋, 帡, 何, 贫,\n",
      "Nearest to 清: 渊, 秧, 沸, 璋, 仇, 精, 怜, 郯,\n",
      "Nearest to 。: ，, 、, ）, 鸪, （, 耘, 淘, 粳,\n",
      "Nearest to 似: 如, 奈, 羡, 是, 共, 记, 癖, 待,\n",
      "Nearest to 如: 似, 于, 逊, 枥, 肓, 憧, 砚, 漱,\n",
      "Average loss at step  302000 :  4.06515221118927\n",
      "Average loss at step  304000 :  4.094218313574791\n",
      "Average loss at step  306000 :  4.097530060648918\n",
      "Average loss at step  308000 :  4.0133272910118105\n",
      "Average loss at step  310000 :  4.009402877569198\n",
      "Nearest to 好: 饶, 佳, 嘉, 慎, 娅, 预, 乐, 巧,\n",
      "Nearest to 何: 无, 踯, 不, 那, 岂, 镞, 多, 翛,\n",
      "Nearest to 得: 取, 著, 了, 怕, 个, 与, 要, 见,\n",
      "Nearest to 情: 意, 恨, 愁, 抨, 诉, 竦, 心, 思,\n",
      "Nearest to 江: 淮, 水, 溪, 廿, 湾, 湘, 湖, 海,\n",
      "Nearest to 来: 去, 今, 鷃, 懒, 伽, 奈, 欤, 又,\n",
      "Nearest to 声: 语, 鸣, 闻, 听, 阕, 笛, 濠, 哀,\n",
      "Nearest to 香: 薰, 熏, 芳, 药, 躞, 醡, 愤, 宅,\n",
      "Nearest to 流: 掀, 莹, 琤, 渝, 祇, 廉, 叫, 乔,\n",
      "Nearest to 愁: 恨, 情, 嗔, 忧, 怨, 怕, 泪, 腥,\n",
      "Nearest to UNK: 忌, C, E, 谢, 崇, ＿, 舵, 泱,\n",
      "Nearest to 多: 噫, 浓, 何, 贫, 深, 籍, 帡, 添,\n",
      "Nearest to 清: 沸, 仇, 秧, 精, 渊, 璋, 朔, 懔,\n",
      "Nearest to 。: ，, 、, ）, ；, 粳, 糠, （, 涔,\n",
      "Nearest to 似: 如, 奈, 是, 羡, 记, 在, 共, 怕,\n",
      "Nearest to 如: 似, 奈, 底, 枥, 弱, 逊, 于, 肓,\n",
      "Average loss at step  312000 :  4.031443693876266\n",
      "Average loss at step  314000 :  3.9809661135673524\n",
      "Average loss at step  316000 :  3.993149571299553\n",
      "Average loss at step  318000 :  4.0324780657291415\n",
      "Average loss at step  320000 :  4.013686353087425\n",
      "Nearest to 好: 佳, 饶, 嘉, 娅, 预, 乐, 慎, 春,\n",
      "Nearest to 何: 镞, 那, 踯, 无, 岂, 忭, 多, 濠,\n",
      "Nearest to 得: 取, 著, 个, 与, 怕, 了, 要, 见,\n",
      "Nearest to 情: 意, 恨, 愁, 抨, 竦, 少, 诉, 心,\n",
      "Nearest to 江: 淮, 水, 溪, 湾, 潇, 廿, 湘, 娅,\n",
      "Nearest to 来: 去, 鷃, 懒, 今, 伽, 奈, 驭, 欤,\n",
      "Nearest to 声: 语, 鸣, 闻, 听, 笛, 叫, 阕, 濠,\n",
      "Nearest to 香: 熏, 薰, 宅, 芳, 雹, 第, 躞, 麟,\n",
      "Nearest to 流: 掀, 莹, 琤, 渝, 乔, 廉, 祇, 改,\n",
      "Nearest to 愁: 恨, 情, 忧, 嗔, 怕, 躞, 感, 泪,\n",
      "Nearest to UNK: 忌, E, C, ＿, 崇, 透, 舵, 泱,\n",
      "Nearest to 多: 噫, 浓, 何, 陋, 添, >, 贫, 籍,\n",
      "Nearest to 清: 秧, 渊, 仇, 辋, 月, 冷, 沸, 懔,\n",
      "Nearest to 。: ，, 、, ）, 鸪, □, 来, 粳, 教,\n",
      "Nearest to 似: 如, 奈, 是, 羡, 记, 共, 怕, 在,\n",
      "Nearest to 如: 似, 巑, 枥, 漱, 间, 底, 逊, 窦,\n",
      "Average loss at step  322000 :  4.053375847458839\n",
      "Average loss at step  324000 :  4.084264584302902\n",
      "Average loss at step  326000 :  4.058848508358002\n",
      "Average loss at step  328000 :  4.077783182621002\n",
      "Average loss at step  330000 :  4.060850519895554\n",
      "Nearest to 好: 佳, 饶, 嘉, 娅, 乐, 慎, 预, 麋,\n",
      "Nearest to 何: 踯, 那, 镞, 岂, 无, 底, 多, 嗷,\n",
      "Nearest to 得: 取, 著, 个, 与, 怕, 了, 要, 疗,\n",
      "Nearest to 情: 意, 愁, 恨, 抨, 竦, 思, 少, 荡,\n",
      "Nearest to 江: 淮, 水, 溪, 湖, 廿, 湘, 湾, 潇,\n",
      "Nearest to 来: 去, 鷃, 懒, 嚲, 又, 今, 奈, 煎,\n",
      "Nearest to 声: 语, 鸣, 闻, 听, 濠, 笛, 悲, 距,\n",
      "Nearest to 香: 熏, 薰, 芳, 葩, 醑, 宅, 愤, 爬,\n",
      "Nearest to 流: 掀, 莹, 渝, 琤, 乔, 姑, 祇, 廉,\n",
      "Nearest to 愁: 恨, 情, 忧, 嗔, 秋, 遽, 怨, 腥,\n",
      "Nearest to UNK: 忌, 崇, E, C, 舵, 透, 掾, ＿,\n",
      "Nearest to 多: 噫, 浓, 深, 帡, 贫, 胜, 肾, 籍,\n",
      "Nearest to 清: 仇, 渊, 璋, 秧, 沸, 精, 良, 懔,\n",
      "Nearest to 。: ，, 、, ）, 鸪, ；, 糠, 攽, 涔,\n",
      "Nearest to 似: 如, 奈, 记, 是, 羡, 共, 棚, 见,\n",
      "Nearest to 如: 似, 砚, 于, 憧, 而, 逊, 枥, 底,\n",
      "Average loss at step  332000 :  4.08393699169159\n",
      "Average loss at step  334000 :  4.0966628484725955\n",
      "Average loss at step  336000 :  3.9926692279577254\n",
      "Average loss at step  338000 :  3.996205661177635\n",
      "Average loss at step  340000 :  4.031683832406998\n",
      "Nearest to 好: 佳, 饶, 嘉, 娅, 慎, 预, 乐, 春,\n",
      "Nearest to 何: 镞, 忭, 踯, 无, 岂, 不, 那, 翛,\n",
      "Nearest to 得: 取, 著, 怕, 了, 个, 见, 与, 把,\n",
      "Nearest to 情: 意, 恨, 愁, 抨, 少, 心, 害, 竦,\n",
      "Nearest to 江: 淮, 水, 溪, 湘, 廿, 海, 湾, 湖,\n",
      "Nearest to 来: 去, 鷃, 懒, 又, 今, 伽, 奈, 欤,\n",
      "Nearest to 声: 语, 鸣, 闻, 听, 阕, 笛, 濠, 叫,\n",
      "Nearest to 香: 熏, 薰, 躞, 芳, 药, 醡, 宅, 愤,\n",
      "Nearest to 流: 莹, 掀, 琤, 渝, 乔, 祇, 廉, 辘,\n",
      "Nearest to 愁: 恨, 情, 嗔, 忧, 泪, 怕, 凄, 腥,\n",
      "Nearest to UNK: 忌, C, E, 崇, ＿, 谢, 泱, 掾,\n",
      "Nearest to 多: 噫, 浓, 贫, 深, 陋, 崆, 何, 帡,\n",
      "Nearest to 清: 仇, 沸, 秧, 一, 闉, 皖, 中, 精,\n",
      "Nearest to 。: ，, 、, ）, 粳, 鸪, ；, 萨, 到,\n",
      "Nearest to 似: 如, 奈, 是, 记, 羡, 在, 为, 待,\n",
      "Nearest to 如: 似, 奈, 于, 底, 枥, 逊, 漱, 间,\n",
      "Average loss at step  342000 :  3.9695682698488235\n",
      "Average loss at step  344000 :  3.981681651651859\n",
      "Average loss at step  346000 :  4.02881284058094\n",
      "Average loss at step  348000 :  4.0036295677423475\n",
      "Average loss at step  350000 :  4.052642235159874\n",
      "Nearest to 好: 佳, 饶, 嘉, 娅, 预, 乐, 麋, 慎,\n",
      "Nearest to 何: 镞, 那, 踯, 多, 忭, 无, 岂, 否,\n",
      "Nearest to 得: 取, 著, 个, 与, 了, 怕, 要, 疗,\n",
      "Nearest to 情: 意, 恨, 抨, 愁, 竦, 少, 蝣, 心,\n",
      "Nearest to 江: 淮, 水, 溪, 湾, 廿, 湘, 湖, 波,\n",
      "Nearest to 来: 去, 鷃, 今, 驭, 又, 奈, 懒, 煎,\n",
      "Nearest to 声: 语, 鸣, 闻, 听, 笛, 阕, 哀, 悲,\n",
      "Nearest to 香: 熏, 薰, 芳, 躞, 宅, 第, 腻, 醑,\n",
      "Nearest to 流: 掀, 琤, 莹, 渝, 廉, 茏, 乔, 拗,\n",
      "Nearest to 愁: 恨, 情, 忧, 嗔, 躞, 怕, 泪, 腥,\n",
      "Nearest to UNK: 忌, E, C, 泱, ＿, 舵, 崇, 透,\n",
      "Nearest to 多: 噫, 浓, 何, 陋, 肾, 贫, 深, >,\n",
      "Nearest to 清: 渊, 秧, 仇, 怜, 辋, 皖, 闉, 沸,\n",
      "Nearest to 。: ，, 、, ）, 萨, 鸪, 粳, ；, 涔,\n",
      "Nearest to 似: 如, 奈, 是, 羡, 记, 在, 共, 胜,\n",
      "Nearest to 如: 似, 间, 枥, 窦, 逊, 巑, 谬, 砚,\n",
      "Average loss at step  352000 :  4.070927003741264\n",
      "Average loss at step  354000 :  4.052424986481666\n",
      "Average loss at step  356000 :  4.0694223144054416\n",
      "Average loss at step  358000 :  4.048105390787125\n",
      "Average loss at step  360000 :  4.077437351465226\n",
      "Nearest to 好: 佳, 饶, 乐, 嘉, 娅, 慎, 预, 麋,\n",
      "Nearest to 何: 踯, 无, 那, 镞, 岂, 忭, 濠, 去,\n",
      "Nearest to 得: 取, 著, 与, 个, 怕, 了, 睚, 要,\n",
      "Nearest to 情: 意, 愁, 恨, 抨, 竦, 思, 少, 恬,\n",
      "Nearest to 江: 淮, 水, 溪, 廿, 湘, 湖, 湾, 截,\n",
      "Nearest to 来: 去, 又, 懒, 鷃, 奈, 今, 欤, 嚲,\n",
      "Nearest to 声: 鸣, 语, 闻, 听, 笛, 濠, 阕, 距,\n",
      "Nearest to 香: 熏, 薰, 葩, 芳, 麝, 爇, 醡, 躞,\n",
      "Nearest to 流: 掀, 莹, 琤, 渝, 茏, 澈, 廉, 祇,\n",
      "Nearest to 愁: 恨, 情, 忧, 嗔, 躞, 怨, 怕, 遽,\n",
      "Nearest to UNK: 忌, C, E, 崇, ＿, 掾, 舵, 透,\n",
      "Nearest to 多: 噫, 浓, 深, 贫, 帡, 陋, 肾, 籍,\n",
      "Nearest to 清: 仇, 渊, 沸, 秧, 璋, 精, 郯, 朔,\n",
      "Nearest to 。: ，, 、, ）, 鸪, ；, 萨, 糠, （,\n",
      "Nearest to 似: 如, 奈, 羡, 是, 记, 见, 棚, 与,\n",
      "Nearest to 如: 似, 砚, 漱, 肓, 枥, 憧, 若, 于,\n",
      "Average loss at step  362000 :  4.08486416721344\n",
      "Average loss at step  364000 :  3.979044372200966\n",
      "Average loss at step  366000 :  3.9909950751066208\n",
      "Average loss at step  368000 :  4.028721978425979\n",
      "Average loss at step  370000 :  3.96370044028759\n",
      "Nearest to 好: 佳, 饶, 嘉, 乐, 娅, 慎, 预, 麋,\n",
      "Nearest to 何: 那, 踯, 镞, 忭, 无, 缥, 岂, 嗷,\n",
      "Nearest to 得: 取, 著, 了, 个, 见, 怕, 与, 把,\n",
      "Nearest to 情: 意, 恨, 愁, 抨, 竦, 害, 少, 名,\n",
      "Nearest to 江: 淮, 水, 溪, 廿, 湘, 湖, 海, 湾,\n",
      "Nearest to 来: 去, 鷃, 懒, 嚲, 伽, 奈, 欤, 又,\n",
      "Nearest to 声: 语, 鸣, 闻, 听, 阕, 笛, 叫, 响,\n",
      "Nearest to 香: 熏, 薰, 宅, 麝, 醡, 雹, 煤, 药,\n",
      "Nearest to 流: 掀, 琤, 莹, 渝, 乔, 拗, 辘, 茏,\n",
      "Nearest to 愁: 恨, 情, 忧, 嗔, 泪, 遽, 凄, 怨,\n",
      "Nearest to UNK: E, 忌, C, 丙, ＿, 掾, 崇, 泱,\n",
      "Nearest to 多: 噫, 浓, 贫, 陋, 深, 肾, 址, 何,\n",
      "Nearest to 清: 沸, 秧, 仇, 一, 朔, 懔, 稏, 闉,\n",
      "Nearest to 。: ，, 、, ）, ；, 粳, 萨, 糠, 涔,\n",
      "Nearest to 似: 如, 奈, 是, 羡, 共, 记, 在, 待,\n",
      "Nearest to 如: 似, 枥, 底, 漱, 于, 随, 逊, 谬,\n",
      "Average loss at step  372000 :  3.9808101752996445\n",
      "Average loss at step  374000 :  4.015693450093269\n",
      "Average loss at step  376000 :  4.000701956868172\n",
      "Average loss at step  378000 :  4.046046004652977\n",
      "Average loss at step  380000 :  4.061211925387383\n",
      "Nearest to 好: 佳, 饶, 嘉, 乐, 娅, 预, 慎, 麋,\n",
      "Nearest to 何: 踯, 镞, 岂, 那, 忭, 多, 无, 庶,\n",
      "Nearest to 得: 取, 著, 个, 怕, 与, 了, 要, 疗,\n",
      "Nearest to 情: 意, 愁, 恨, 抨, 竦, 少, 思, 兴,\n",
      "Nearest to 江: 淮, 水, 溪, 廿, 湘, 湾, 截, 波,\n",
      "Nearest to 来: 去, 鷃, 煎, 驭, 懒, 奈, 嚲, 诸,\n",
      "Nearest to 声: 语, 鸣, 闻, 听, 笛, 叫, 阕, 哀,\n",
      "Nearest to 香: 熏, 薰, 宅, 芳, 凉, 葩, 躞, 醑,\n",
      "Nearest to 流: 掀, 莹, 琤, 渝, 拗, 迅, 姑, 龊,\n",
      "Nearest to 愁: 恨, 情, 忧, 嗔, 躞, 秋, 怕, 腥,\n",
      "Nearest to UNK: 忌, E, C, ＿, 舵, 掾, 食, 崇,\n",
      "Nearest to 多: 噫, 浓, 肾, 贫, 陋, 深, 何, 帡,\n",
      "Nearest to 清: 渊, 仇, 秧, 璋, 沸, 一, 凉, 皖,\n",
      "Nearest to 。: ，, 、, ）, 楷, 筲, 粳, 匮, 岐,\n",
      "Nearest to 似: 如, 奈, 羡, 是, 记, 胜, 癖, 在,\n",
      "Nearest to 如: 似, 逊, 窦, 砚, 肓, 枥, 漱, 于,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  382000 :  4.044886782646179\n",
      "Average loss at step  384000 :  4.05606707251072\n",
      "Average loss at step  386000 :  4.050802290797233\n",
      "Average loss at step  388000 :  4.067499099731445\n",
      "Average loss at step  390000 :  4.080472494721413\n",
      "Nearest to 好: 饶, 佳, 娅, 乐, 麋, 慎, 嘉, 预,\n",
      "Nearest to 何: 那, 踯, 岂, 镞, 无, 多, 忭, 不,\n",
      "Nearest to 得: 取, 著, 怕, 与, 了, 见, 个, 要,\n",
      "Nearest to 情: 意, 恨, 愁, 抨, 思, 竦, 少, 恬,\n",
      "Nearest to 江: 淮, 水, 溪, 湘, 廿, 海, 湖, 湾,\n",
      "Nearest to 来: 去, 懒, 鷃, 又, 奈, 嚲, 今, 欤,\n",
      "Nearest to 声: 鸣, 语, 闻, 听, 笛, 阕, 叫, 响,\n",
      "Nearest to 香: 熏, 薰, 芳, 宅, 醑, 葩, 爬, 躞,\n",
      "Nearest to 流: 掀, 莹, 琤, 渝, 祇, 廉, 摇, 迅,\n",
      "Nearest to 愁: 恨, 情, 忧, 嗔, 泪, 腥, 心, 遽,\n",
      "Nearest to UNK: 忌, 崇, E, ＿, 舵, C, 毵, 掾,\n",
      "Nearest to 多: 噫, 贫, 浓, 何, 深, 簿, 闲, 帡,\n",
      "Nearest to 清: 仇, 渊, 璋, 月, 朔, 懔, 皖, 秧,\n",
      "Nearest to 。: ，, 、, ）, 鸪, 粳, ；, 诳, 云,\n",
      "Nearest to 似: 如, 是, 奈, 羡, 记, 见, 与, 共,\n",
      "Nearest to 如: 似, 砚, 漱, 枥, 逊, 而, 于, 奈,\n",
      "Average loss at step  392000 :  3.9680935555696486\n",
      "Average loss at step  394000 :  3.9860730654001237\n",
      "Average loss at step  396000 :  4.015346494913101\n",
      "Average loss at step  398000 :  3.9599427200555803\n",
      "Average loss at step  400000 :  3.9716230794787406\n",
      "Nearest to 好: 佳, 饶, 乐, 嘉, 娅, 慎, 春, 预,\n",
      "Nearest to 何: 镞, 那, 踯, 无, 忭, 岂, 庶, 多,\n",
      "Nearest to 得: 取, 著, 与, 见, 个, 了, 怕, 要,\n",
      "Nearest to 情: 意, 恨, 愁, 抨, 竦, 少, 诉, 心,\n",
      "Nearest to 江: 淮, 水, 溪, 湘, 湾, 廿, 湖, 海,\n",
      "Nearest to 来: 去, 鷃, 懒, 今, 又, 嚲, 奈, 欤,\n",
      "Nearest to 声: 语, 鸣, 闻, 听, 阕, 笛, 哀, 叫,\n",
      "Nearest to 香: 熏, 薰, 芳, 雹, 宅, 麝, 躞, 醡,\n",
      "Nearest to 流: 掀, 莹, 琤, 渝, 拗, 龊, 溅, 祇,\n",
      "Nearest to 愁: 恨, 忧, 嗔, 情, 遽, 泪, 凄, 怕,\n",
      "Nearest to UNK: E, 忌, ＿, 崇, 舵, 掾, 丙, C,\n",
      "Nearest to 多: 噫, 浓, 贫, 深, 陋, 何, 肾, 帡,\n",
      "Nearest to 清: 沸, 秧, 仇, 渊, 璋, 稏, 一, 好,\n",
      "Nearest to 。: ，, 、, ）, 鸪, 萨, ；, 攽, 瀚,\n",
      "Nearest to 似: 如, 奈, 是, 羡, 在, 胜, 共, 记,\n",
      "Nearest to 如: 似, 枥, 逊, 弱, 巑, 肓, 漱, 间,\n"
     ]
    }
   ],
   "source": [
    "# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "# ==============================================================================\n",
    "\"\"\"Basic word2vec example.\"\"\"\n",
    "\n",
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import collections\n",
    "import math\n",
    "import os\n",
    "import random\n",
    "from tempfile import gettempdir\n",
    "import zipfile\n",
    "import json\n",
    "\n",
    "import numpy as np\n",
    "from six.moves import urllib\n",
    "from six.moves import xrange  # pylint: disable=redefined-builtin\n",
    "import tensorflow as tf\n",
    "\n",
    "\n",
    "filename = 'QuanSongCi.txt'\n",
    "# Read the data into a list of strings.\n",
    "def read_data(filename):\n",
    " \n",
    "  lines = []\n",
    "  with open(filename,\"r\",encoding=\"UTF-8\") as f:\n",
    "        line = f.readline() # 逐行读入\n",
    "        while line:\n",
    "            while '\\n' in line:\n",
    "                line = line.replace('\\n','') # 换行符替换\n",
    "            lines.append(line)\n",
    "            line=f.readline()\n",
    "  words = []\n",
    "  for lw in lines:\n",
    "        words += [w for w in lw]\n",
    "  return words\n",
    "    \n",
    "\n",
    "vocabulary = read_data(filename)\n",
    "print('Data size', len(vocabulary))\n",
    "\n",
    "# Step 1: Build the dictionary and replace rare words with UNK token.\n",
    "vocabulary_size = 5000\n",
    "\n",
    "\n",
    "def build_dataset(words, n_words):\n",
    "  \"\"\"Process raw inputs into a dataset.\"\"\"\n",
    "  count = [['UNK', -1]]\n",
    "  count.extend(collections.Counter(words).most_common(n_words - 1))\n",
    "  dictionary = dict()\n",
    "  for word, _ in count:\n",
    "    dictionary[word] = len(dictionary)\n",
    "  data = list()\n",
    "  unk_count = 0\n",
    "  for word in words:\n",
    "    index = dictionary.get(word, 0)\n",
    "    if index == 0:  # dictionary['UNK']\n",
    "      unk_count += 1\n",
    "    data.append(index)\n",
    "  count[0][1] = unk_count\n",
    "  reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\n",
    "  return data, count, dictionary, reversed_dictionary\n",
    "\n",
    "# Filling 4 global variables:\n",
    "# data - list of codes (integers from 0 to vocabulary_size-1).\n",
    "#   This is the original text but words are replaced by their codes\n",
    "# count - map of words(strings) to count of occurrences\n",
    "# dictionary - map of words(strings) to their codes(integers)\n",
    "# reverse_dictionary - maps codes(integers) to words(strings)\n",
    "data, count, dictionary, reverse_dictionary = build_dataset(vocabulary,\n",
    "                                                            vocabulary_size)\n",
    "\n",
    "# 将dictionary, reverse_dictionary保存成json文件\n",
    "dictionary_name = 'dictionary.json'\n",
    "reverse_dictionary_name = 'reverse_dictionary.json'\n",
    "\n",
    "with open(dictionary_name,'w') as dictionary_object:\n",
    "    json.dump(dictionary,dictionary_object)\n",
    "    \n",
    "with open(reverse_dictionary_name,'w') as reverse_dictionary_object:\n",
    "    json.dump(reverse_dictionary,reverse_dictionary_object)\n",
    "\n",
    "del vocabulary  # Hint to reduce memory.\n",
    "print('Most common words (+UNK)', count[:5])\n",
    "print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])\n",
    "\n",
    "data_index = 0\n",
    "\n",
    "# Step 2: Function to generate a training batch for the skip-gram model.\n",
    "def generate_batch(batch_size, num_skips, skip_window):\n",
    "  global data_index\n",
    "  assert batch_size % num_skips == 0\n",
    "  assert num_skips <= 2 * skip_window\n",
    "  batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n",
    "  labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n",
    "  span = 2 * skip_window + 1  # [ skip_window target skip_window ]\n",
    "  buffer = collections.deque(maxlen=span)\n",
    "  if data_index + span > len(data):\n",
    "    data_index = 0\n",
    "  buffer.extend(data[data_index:data_index + span])\n",
    "  data_index += span\n",
    "  for i in range(batch_size // num_skips):\n",
    "    context_words = [w for w in range(span) if w != skip_window]\n",
    "    words_to_use = random.sample(context_words, num_skips)\n",
    "    for j, context_word in enumerate(words_to_use):\n",
    "      batch[i * num_skips + j] = buffer[skip_window]\n",
    "      labels[i * num_skips + j, 0] = buffer[context_word]\n",
    "    if data_index == len(data):\n",
    "      #buffer[:] = data[:span]\n",
    "      # TypeError:sequence index must be integer, not 'slice'\n",
    "      for w in data[:span]:\n",
    "        buffer.append(w)\n",
    "        data_index = span\n",
    "      \n",
    "    else:\n",
    "      buffer.append(data[data_index])\n",
    "      data_index += 1\n",
    "  # Backtrack a little bit to avoid skipping words in the end of a batch\n",
    "  data_index = (data_index + len(data) - span) % len(data)\n",
    "  return batch, labels\n",
    "\n",
    "batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)\n",
    "for i in range(8):\n",
    "  print(batch[i], reverse_dictionary[batch[i]],\n",
    "        '->', labels[i, 0], reverse_dictionary[labels[i, 0]])\n",
    "\n",
    "# Step 3: Build and train a skip-gram model.\n",
    "\n",
    "batch_size = 128\n",
    "embedding_size = 128  # Dimension of the embedding vector.\n",
    "skip_window = 1       # How many words to consider left and right.\n",
    "num_skips = 2         # How many times to reuse an input to generate a label.\n",
    "num_sampled = 64      # Number of negative examples to sample.\n",
    "\n",
    "# We pick a random validation set to sample nearest neighbors. Here we limit the\n",
    "# validation samples to the words that have a low numeric ID, which by\n",
    "# construction are also the most frequent. These 3 variables are used only for\n",
    "# displaying model accuracy, they don't affect calculation.\n",
    "valid_size = 16     # Random set of words to evaluate similarity on.\n",
    "valid_window = 100  # Only pick dev samples in the head of the distribution.\n",
    "valid_examples = np.random.choice(valid_window, valid_size, replace=False)\n",
    "\n",
    "\n",
    "graph = tf.Graph()\n",
    "\n",
    "with graph.as_default():\n",
    "\n",
    "  # Input data.\n",
    "  train_inputs = tf.placeholder(tf.int32, shape=[batch_size])\n",
    "  train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\n",
    "  valid_dataset = tf.constant(valid_examples, dtype=tf.int32)\n",
    "\n",
    "  # Ops and variables pinned to the CPU because of missing GPU implementation\n",
    "  with tf.device('/cpu:0'):\n",
    "    # Look up embeddings for inputs.\n",
    "    embeddings = tf.Variable(\n",
    "        tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))\n",
    "    embed = tf.nn.embedding_lookup(embeddings, train_inputs)\n",
    "\n",
    "    # Construct the variables for the NCE loss\n",
    "    nce_weights = tf.Variable(\n",
    "        tf.truncated_normal([vocabulary_size, embedding_size],\n",
    "                            stddev=1.0 / math.sqrt(embedding_size)))\n",
    "    nce_biases = tf.Variable(tf.zeros([vocabulary_size]))\n",
    "\n",
    "  # Compute the average NCE loss for the batch.\n",
    "  # tf.nce_loss automatically draws a new sample of the negative labels each\n",
    "  # time we evaluate the loss.\n",
    "  # Explanation of the meaning of NCE loss:\n",
    "  #   http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/\n",
    "  loss = tf.reduce_mean(\n",
    "      tf.nn.nce_loss(weights=nce_weights,\n",
    "                     biases=nce_biases,\n",
    "                     labels=train_labels,\n",
    "                     inputs=embed,\n",
    "                     num_sampled=num_sampled,\n",
    "                     num_classes=vocabulary_size))\n",
    "\n",
    "  # Construct the SGD optimizer using a learning rate of 1.0.\n",
    "  optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)\n",
    "\n",
    "  # Compute the cosine similarity between minibatch examples and all embeddings.\n",
    "  norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))\n",
    "  normalized_embeddings = embeddings / norm\n",
    "  valid_embeddings = tf.nn.embedding_lookup(\n",
    "      normalized_embeddings, valid_dataset)\n",
    "  similarity = tf.matmul(\n",
    "      valid_embeddings, normalized_embeddings, transpose_b=True)\n",
    "\n",
    "  # Add variable initializer.\n",
    "  init = tf.global_variables_initializer()\n",
    "\n",
    "# Step 4: Begin training.\n",
    "num_steps = 400001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "  # We must initialize all variables before we use them.\n",
    "  init.run()\n",
    "  print('Initialized')\n",
    "\n",
    "  average_loss = 0\n",
    "  for step in xrange(num_steps):\n",
    "    batch_inputs, batch_labels = generate_batch(\n",
    "        batch_size, num_skips, skip_window)\n",
    "    feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}\n",
    "\n",
    "    # We perform one update step by evaluating the optimizer op (including it\n",
    "    # in the list of returned values for session.run()\n",
    "    _, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)\n",
    "    average_loss += loss_val\n",
    "\n",
    "    if step % 2000 == 0:\n",
    "      if step > 0:\n",
    "        average_loss /= 2000\n",
    "      # The average loss is an estimate of the loss over the last 2000 batches.\n",
    "      print('Average loss at step ', step, ': ', average_loss)\n",
    "      average_loss = 0\n",
    "\n",
    "    # Note that this is expensive (~20% slowdown if computed every 500 steps)\n",
    "    if step % 10000 == 0:\n",
    "      sim = similarity.eval()\n",
    "      for i in xrange(valid_size):\n",
    "        valid_word = reverse_dictionary[valid_examples[i]]\n",
    "        top_k = 8  # number of nearest neighbors\n",
    "        nearest = (-sim[i, :]).argsort()[1:top_k + 1]\n",
    "        log_str = 'Nearest to %s:' % valid_word\n",
    "        for k in xrange(top_k):\n",
    "          close_word = reverse_dictionary[nearest[k]]\n",
    "          log_str = '%s %s,' % (log_str, close_word)\n",
    "        print(log_str)\n",
    "  final_embeddings = normalized_embeddings.eval()\n",
    "\n",
    "# word2vec中，可以使用如下代码来保存最终生成的embeding\n",
    "  np.save('embedding.npy', final_embeddings)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-10T07:43:39.692431Z",
     "start_time": "2019-02-10T07:42:56.735062Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 5: Visualize the embeddings.\n",
    "\n",
    "\n",
    "# pylint: disable=missing-docstring\n",
    "# Function to draw visualization of distance between embeddings.\n",
    "def plot_with_labels(low_dim_embs, labels, filename):\n",
    "  assert low_dim_embs.shape[0] >= len(labels), 'More labels than embeddings'\n",
    "  plt.figure(figsize=(18, 18))  # in inches\n",
    "  for i, label in enumerate(labels):\n",
    "    x, y = low_dim_embs[i, :]\n",
    "    plt.scatter(x, y)\n",
    "    plt.annotate(label,\n",
    "                 xy=(x, y),\n",
    "                 xytext=(5, 2),\n",
    "                 textcoords='offset points',\n",
    "                 ha='right',\n",
    "                 va='bottom')\n",
    "\n",
    "  plt.savefig(filename)\n",
    "\n",
    "try:\n",
    "  # pylint: disable=g-import-not-at-top\n",
    "  from sklearn.manifold import TSNE\n",
    "  import matplotlib.pyplot as plt\n",
    "  \n",
    "    \n",
    " \n",
    "  plt.rcParams['font.sans-serif'] = ['SimHei']  # 中文字体设置  \n",
    "  plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "  tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')\n",
    "  plot_only = 500\n",
    "  low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])\n",
    "  labels = [reverse_dictionary[i] for i in range(plot_only)]\n",
    "  plot_with_labels(low_dim_embs, labels, os.path.join(gettempdir(), 'tsne.png'))\n",
    "\n",
    "except ImportError as ex:\n",
    "  print('Please install sklearn, matplotlib, and scipy to show embeddings.')\n",
    "  print(ex)\n",
    "  "
   ]
  }
 ],
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